Autor(es):
Pinto, Tiago W. ; Carvalho, Marco A. G. de ; Pedronette, Daniel C. G. [UNESP] ; Martins, Paulo S. ; IEEE
Data: 2015
Identificador Persistente: http://hdl.handle.net/11449/130056
Origem: Oasisbr
Assunto(s): Image segmentation; Watershed transform; Graph partitioning; Normalized cut; Unsupervised distance learning; Image segmentation; Image segmentation; Watershed transform; Watershed transform; Graph partitioning; Graph partitioning; Normalized cut; Normalized cut; Unsupervised distance learning; Unsupervised distance learning
Descrição
Made available in DSpace on 2015-11-03T15:28:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-01-01
Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.
School of Technology, UNICAMP, Limeira, São Paulo, Brazil.
Universidade Estadual Paulista, Department of Statistics, Applied Mathematics and Computing, BR-13506900 São Paulo, Brazil
